This letter presents a cooperative relative multi-robot localization design and experimental study. We propose a flexible Monte Carlo approach leveraging a particle filter to estimate relative states. The estimation can be based on inter-robot Ultra-Wideband (UWB) ranging and onboard odometry alone or dynamically integrated with cooperative spatial object detections from stereo cameras mounted on each robot. The main contributions of this work are as follows. First, we show that a single UWB range is enough to estimate the accurate relative states of two robots when fusing odometry measurements. Second, our experiments also demonstrate that our approach surpasses traditional methods, namely, multilateration, in terms of accuracy. Third, to further increase accuracy, we allow for the integration of cooperative spatial detections. Finally, we show how ROS 2 and Zenoh can be integrated to build a scalable wireless communication solution for multi-robot systems. The experimental validation includes real-time deployment and autonomous navigation based on the relative positioning method. It is worth mentioning that we also address the challenges for UWB-ranging error mitigation for mobile transceivers. The code is available at https://github.com/TIERS/uwb-cooperative-mrs-localization.

翻译：本文提出一种协作式多机器人相对定位的设计与实验研究。我们采用一种灵活的蒙特卡洛方法，利用粒子滤波器估计相对状态。该估计可仅基于机器人间超宽带测距与车载里程计，或动态集成各机器人搭载的立体相机协作式空间目标检测结果。本研究主要贡献如下：第一，证明融合里程计测量值时，单个UWB测距即可精确估计两台机器人的相对状态；第二，实验表明本方法在精度上超越传统多边定位法；第三，为进一步提升精度，我们允许集成协作式空间检测；第四，展示如何整合ROS 2与Zenoh构建多机器人系统的可扩展无线通信方案。实验验证包括基于该相对定位方法的实时部署与自主导航。值得强调的是，我们还解决了移动收发器的UWB测距误差修正问题。代码已开源至https://github.com/TIERS/uwb-cooperative-mrs-localization。